Biology subjects

Kelly, D.

Publications and source records attributed to Kelly, D..

4 recordsLinked to original sources

bioRxiv2018

Laboratory and Molecular Surveillance of Paediatric Typhoidal Salmonella in Nepal: Antimicrobial Resistance and Implications for Vaccine Policy

BackgroundChildren are substantially affected by enteric fever in most settings with a high burden of the disease, which could be due to immune naivety, or enhanced risk of exposure to the pathogen. Although Nepal is a high burden setting for enteric fever, the bacterial population structure and transmission dynamics are poorly delineated in young children, the proposed target group for immunization programs.\n\nMethodsBlood culture surveillance amongst children aged 2 months to 15 years of age was conducted at Patan Hospital between 2008 and 2016. A total of 198 S. Typhi and 66 S. Paratyphi A isolated from children treated in both inpatient and outpatient settings were subjected to whole genome sequencing and antimicrobial susceptibility testing. Demographic and clinical data were also collected from the inpatients. The resulting data were used to place these paediatric Nepali isolates into a worldwide context, based on their phylogeny and carriage of molecular determinants of antimicrobial resistance (AMR).\n\nResultsChildren aged [≤]4 years made up >40% of the inpatient population. The majority of isolates (78 %) were S. Typhi, comprising several distinct genotypes but dominated by 4.3.1 (H58). Several distinct S. Typhi genotypes were identified, but the globally disseminated S. Typhi clade 4.3.1 (H58) dominated. The majority of isolates (86%) were insusceptible to fluoroquinolones. This was mainly associated with S. Typhi H58 Lineage II and S. Paratyphi A; non-susceptible strains from these two genotypes accounted for 50% and 25% of all enteric fever cases. Multi-drug resistance (MDR) was rare (3.5% of S. Typhi, 0 S. Paratyphi A) and restricted to chromosomal insertions of AMR genes in H58 lineage I strains. Comparison to global data sets showed the local S. Typhi and S. Paratyphi A strains had close genetic relatives in other South Asian countries, indicating regional strain circulation.\n\nConclusionsThese data indicate that enteric fever in Nepal continues to be a major public health issue with ongoing inter- and intra-country transmission, and highlights the need for regional coordination of intervention strategies. The absence of a S. Paratyphi A vaccine is cause for concern, given its prevalence as an enteric fever agent in this setting, and the large proportion of isolates displaying fluoroquinolone resistance. This study also highlights an urgent need for routine laboratory and molecular surveillance to monitor the epidemiology of enteric fever and evolution of antimicrobial resistance within the bacterial population as a means to facilitate public health interventions in prevention and control of this febrile illness.

molecular biology↗

bioRxiv2017

HPViewer: Sensitive and specific genotyping of human papillomavirus in metagenomic DNA

BackgroundShotgun DNA sequencing provides sensitive detection of all 182 HPV types in tissue and body fluid. However, existing computational methods either produce false positives misidentifying HPV types due to shared sequences among HPV, human, and prokaryotes, or produce false negative since they identify HPV by assembled contigs requiring large abundant of HPV reads.\n\nResultsWe show that HPV shares extensive simple repeats with human and prokaryotes and homologous sequences among different HPV types. The shared sequences caused errors in HPV genotyping and the repeats of human origin caused false positives in HPVDetector. Programs, such as VirusTAP and Vipie, which require de novo assembly of shotgun reads into contigs, eliminated false positives at a cost of substantial reduction in sensitivity. Here, we designed HPViewer with two custom HPV reference databases masking simple repeats and homology sequences respectively and one homology distance matrix to hybridize these two databases. It directly identified HPV from short DNA reads rather than assembled contigs. Using 100,100 simulated samples, we revealed that HPViewer was robust for samples containing either high or low number of HPV reads. Using 12 shotgun sequencing samples from respiratory papillomatosis, HPViewer was equal to VirusTAP, and Vipie and better than HPVDetector with the respect to specificity and was the most sensitive method in the detection of HPV types 6 and 11. We demonstrated that contigs-based approaches had disadvantages of detection of HPV. In 1,573 sets of metagenomic data from 18 human body sites, HPViewer identified 104 types of HPV in a body-site associated pattern and 89 types of HPV co-occurring in one sample with other types of HPV at least once.\n\nConclusionsWe demonstrated HPViewer was sensitive and specific for HPV detection in metagenomic data. It was also suggested that masking shared sequences is an effective approach to avoid false positive detection and identifying HPV from short metagenomic reads is more sensitive than assembled contigs. The innovative homology distance matrix connecting two HPV databases, repeat-mask and homology-mask, optimized the balance of sensitivity and specificity. HPViewer can be accessed at https://github.com/yuhanH/HPViewer/.

bioinformatics↗

bioRxiv2017

Accounting For Technical Noise In Single-Cell RNA Sequencing Analysis

Recent technological breakthroughs have made it possible to measure RNA expression at the single-cell level, thus paving the way for exploring expression heterogeneity among individual cells. Current single-cell RNA sequencing (scRNA-seq) protocols are complex and introduce technical biases that vary across cells, which can bias downstream analysis without proper adjustment. To account for cell-to-cell technical differences, we propose a statistical framework, TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. More importantly, TASC is able to adjust for covariates to further eliminate confounding that may originate from cell size and cell cycle differences. In simulation and real scRNA-seq data, TASC achieves accurate Type I error control and displays competitive sensitivity and improved robustness to batch effects in differential expression analysis, compared to existing methods. TASC is programmed to be computationally efficient, taking advantage of multi-threaded parallelization. We believe that TASC will provide a robust platform for researchers to leverage the power of scRNA-seq.

bioinformatics↗

bioRxiv2016

Splice Expression Variation Analysis (SEVA) for Differential Gene Isoform Usage in Cancer

MotivationCurrent bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches.\n\nResultsWe introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g., tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVAs performance against EBSeq, DiffSplice, and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVAin identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery.\n\nConclusionThese results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data.\n\nAvailabilitySEVA is implemented in the R/Bioconductor package GSReg.\n\nContactbahman@jhu.edu, favorov@sensi.org, ejfertig@jhmi.edu

genomics↗